Abstract
In this paper, a new dynamic multi-objective optimization evolutionary algorithm is proposed for tracking the Pareto-optimal set of time-changing multi-objective optimization problems effectively. In the proposed algorithm, to select individuals which are best suited for a new time from the historical optimal sets, an orthogonal predictive model is presented to predict the new individuals after the environment change is detected. Also, to converge to optimal front more quickly, an modified multi-objective optimization evolutionary algorithm based on decomposition is adopted. The proposed method has been extensively compared with other three dynamic multi-objective evolutionary algorithms over several benchmark dynamic multi-objective optimization problems. The experimental results indicate that the proposed algorithm achieves competitive results.
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Acknowledgments
The authors would like to thank the editor and the reviewers for helpful comments that greatly improved the paper. This work was supported by the National Natural Science Foundation of China (Nos. 61373111, 61272279, 61003199 and 61203303), the Fundamental Research Funds for the Central University (Nos. K50511020014, K5051302084, K50510020011, K5051302049, and K5051302023); the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048), the Program for Century Excellent Talents in University (No. NCET-12-0920), and the Provincial Natural Science Foundation of Shaanxi of China (no. 2014JM8321).
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Liu, R., Niu, X., Fan, J. et al. An orthogonal predictive model-based dynamic multi-objective optimization algorithm. Soft Comput 19, 3083–3107 (2015). https://doi.org/10.1007/s00500-014-1470-y
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DOI: https://doi.org/10.1007/s00500-014-1470-y